Working towards a better understanding of type 2 diabetes care organization with First Nations communities: a qualitative assessment
Bibliographic record
Abstract
BACKGROUND: ) model is one strategy to improve care: an integrated disease registry and electronic health record for community healthcare workers with centralized care coordination. The aim of this study was to qualitatively assess the organization of type 2 diabetes care in participating communities in Alberta, Canada, at baseline prior to implementing RADAR. METHODS: Using qualitative description, we purposefully sampled healthcare workers involved in diabetes care at each health center. We used the 5Rs framework (i.e., Recognize, Register, Resource, Relay, Recall) to inform the baseline assessment and conducted group interviews in 6 communities with 16 healthcare workers. Detailed notes were taken and validated by participants. Data was managed using ATLAS.ti 8 and analyzed using content analysis. RESULTS: We found strong commitment and effort by local healthcare workers to support people living with type 2 diabetes in their communities. However, healthcare workers were limited in their ability to identify (i.e., recognize), track (i.e., register and relay) and manage (i.e., resource and recall) people with type 2 diabetes as proposed by the 5Rs framework. The organization of diabetes care was often reactive and dependent on patients' abilities to navigate the health system. Interestingly, participants talked about the 5Rs in relationship to one another, not in a linear or isolated manner. CONCLUSIONS: Overall, the organization of diabetes care in participating communities did not align with the recommended approach of the 5Rs framework. In addition, we propose "reimagining" the 5Rs to reflect the interdependence and mediation of components situated within human and financial resources. This will better equip healthcare workers to assess, plan and execute organized and proactive diabetes care. However, the onus on people living with type 2 diabetes to engage with healthcare services remains a concern. TRIAL REGISTRATION: ISRCTN.com, ISRCTN14359671.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".